Surface plasmon resonance (SPR) measurement systems rely on surface plasmon resonances to detect changes in refractive index of a sample, or target, proximate to a transducing interface. Due to the surface plasmon resonance phenomenon, optical signals that are incident on, and deflected at, the transducing interface undergo a loss at a resonant incidence angle and a resonant optical wavelength. Changes in the refractive index of the sample cause changes in the resonant incidence angle and the resonant optical wavelength. An SPR measurement system relates detected changes in the resonant incidence angle or resonant optical wavelength to corresponding changes in the refractive index of the sample, typically in the form of an SPR sensorgram, which is a plot of the relative refractive index of the sample versus time.
While SPR sensorgrams can be used to characterize biochemical processes within the samples based on relationships between the biochemical processes and refractive indices of the samples, the SPR sensorgrams include noise components that can mask or otherwise distort features of the SPR sensorgrams. Typically, the noise components are attributed to uncertainty in the measurement of relative refractive index of the samples by the SPR measurement system, or to mechanical events in the SPR measurement system, such as the opening or closing of valves that control the flow of buffer and analytes in the samples.
Linear filtering is commonly used in signal processing to reduce noise components that are present on a signal. However, when linear filtering, such as lowpass filtering, is applied to an SPR sensorgram, high frequency features, such as sharp transitions in the SPR sensorgram are smoothed out, or eliminated. However, the sharp transitions may be indicative of a critical biochemical process or event, such as the onset of a binding event between analytes and ligands within the sample. Smoothing out or eliminating the sharp transitions in the SPR sensorgram may make it difficult to determine association/dissociation rates and other important indicators of biochemical processes. Linear filtering can also result in ringing when a signal includes outlying data points, discontinuities, or other anomalies, making biochemical processes or events depicted in the SPR sensorgram difficult to interpret. Filters based on Fourier Transforms, FIR (finite impulse response) filters, IIR (infinite impulse response) filters, and numerous other classes of linear filters may not be well-suited for reducing noise components in SPR sensorgrams due to the signal features of the SPR sensorgrams and the resulting shortcomings of the linear filters when applied to the SPR sensorgrams.